Overview

Dataset statistics

Number of variables26
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.8 KiB
Average record size in memory208.6 B

Variable types

Numeric16
Text1
Categorical9

Alerts

fueltype is highly imbalanced (53.9%)Imbalance
enginelocation is highly imbalanced (89.0%)Imbalance
cylindernumber is highly imbalanced (57.6%)Imbalance
car_ID is uniformly distributedUniform
car_ID has unique valuesUnique
symboling has 67 (32.7%) zerosZeros

Reproduction

Analysis started2024-05-12 05:43:08.414684
Analysis finished2024-05-12 05:44:23.079503
Duration1 minute and 14.66 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

car_ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:23.592647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.2
Q152
median103
Q3154
95-th percentile194.8
Maximum205
Range204
Interquartile range (IQR)102

Descriptive statistics

Standard deviation59.322565
Coefficient of variation (CV)0.57594723
Kurtosis-1.2
Mean103
Median Absolute Deviation (MAD)51
Skewness0
Sum21115
Variance3519.1667
MonotonicityStrictly increasing
2024-05-12T09:14:24.091900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
142 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
Other values (195) 195
95.1%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%

symboling
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2024-05-12T09:14:24.573125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2024-05-12T09:14:24.966051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%
Distinct147
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:25.996726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length31
Median length24
Mean length14.146341
Min length6

Characters and Unicode

Total characters2900
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)53.2%

Sample

1st rowalfa-romero giulia
2nd rowalfa-romero stelvio
3rd rowalfa-romero Quadrifoglio
4th rowaudi 100 ls
5th rowaudi 100ls
ValueCountFrequency (%)
toyota 31
 
6.4%
nissan 18
 
3.7%
mazda 15
 
3.1%
mitsubishi 13
 
2.7%
honda 13
 
2.7%
corolla 12
 
2.5%
subaru 12
 
2.5%
peugeot 11
 
2.3%
volvo 11
 
2.3%
sw 10
 
2.0%
Other values (167) 342
70.1%
2024-05-12T09:14:27.544998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
285
 
9.8%
a 259
 
8.9%
o 243
 
8.4%
t 167
 
5.8%
e 158
 
5.4%
s 153
 
5.3%
i 147
 
5.1%
l 138
 
4.8%
r 133
 
4.6%
c 126
 
4.3%
Other values (36) 1091
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
285
 
9.8%
a 259
 
8.9%
o 243
 
8.4%
t 167
 
5.8%
e 158
 
5.4%
s 153
 
5.3%
i 147
 
5.1%
l 138
 
4.8%
r 133
 
4.6%
c 126
 
4.3%
Other values (36) 1091
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
285
 
9.8%
a 259
 
8.9%
o 243
 
8.4%
t 167
 
5.8%
e 158
 
5.4%
s 153
 
5.3%
i 147
 
5.1%
l 138
 
4.8%
r 133
 
4.6%
c 126
 
4.3%
Other values (36) 1091
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
285
 
9.8%
a 259
 
8.9%
o 243
 
8.4%
t 167
 
5.8%
e 158
 
5.4%
s 153
 
5.3%
i 147
 
5.1%
l 138
 
4.8%
r 133
 
4.6%
c 126
 
4.3%
Other values (36) 1091
37.6%

fueltype
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Length

2024-05-12T09:14:27.993188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:28.332214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

aspiration
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
168 
turbo
37 

Length

Max length5
Median length3
Mean length3.3609756
Min length3

Characters and Unicode

Total characters689
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Length

2024-05-12T09:14:28.900264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:29.491675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

doornumber
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
115 
two
90 

Length

Max length4
Median length4
Mean length3.5609756
Min length3

Characters and Unicode

Total characters730
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 115
56.1%
two 90
43.9%

Length

2024-05-12T09:14:29.961748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:30.365077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
four 115
56.1%
two 90
43.9%

Most occurring characters

ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

carbody
Categorical

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6195122
Min length5

Characters and Unicode

Total characters1357
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Length

2024-05-12T09:14:30.802831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:31.149915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

drivewheel
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
120 
rwd
76 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Length

2024-05-12T09:14:31.563859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:31.944773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

enginelocation
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
202 
rear
 
3

Length

Max length5
Median length5
Mean length4.9853659
Min length4

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Length

2024-05-12T09:14:32.393457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:32.971450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

wheelbase
Real number (ℝ)

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:33.578032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2024-05-12T09:14:34.107119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.2%
93.7 20
 
9.8%
95.7 13
 
6.3%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
104.3 6
 
2.9%
100.4 6
 
2.9%
107.9 6
 
2.9%
98.8 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.4%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.4%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

carlength
Real number (ℝ)

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:34.566277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2024-05-12T09:14:35.028600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.3%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
186.6 6
 
2.9%
172 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.3%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

carwidth
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:35.568897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2024-05-12T09:14:36.102941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.7%
66.5 23
 
11.2%
65.4 15
 
7.3%
63.6 11
 
5.4%
64.4 10
 
4.9%
68.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.7%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

carheight
Real number (ℝ)

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:36.564143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2024-05-12T09:14:37.113412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.8%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.4%
50.8 14
6.8%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

curbweight
Real number (ℝ)

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:37.625797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2024-05-12T09:14:38.097550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

enginetype
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2024-05-12T09:14:38.604976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:39.038977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

cylindernumber
Categorical

IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2024-05-12T09:14:39.668540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:40.031858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

enginesize
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:40.429648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2024-05-12T09:14:40.869379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.3%
92 15
 
7.3%
97 14
 
6.8%
98 14
 
6.8%
108 13
 
6.3%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.4%
92 15
7.3%
97 14
6.8%
98 14
6.8%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%

fuelsystem
Categorical

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2024-05-12T09:14:41.252036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T09:14:41.649820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

boreratio
Real number (ℝ)

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3297561
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:42.065569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27084371
Coefficient of variation (CV)0.081340404
Kurtosis-0.78504183
Mean3.3297561
Median Absolute Deviation (MAD)0.26
Skewness0.020156418
Sum682.6
Variance0.073356313
MonotonicityNot monotonic
2024-05-12T09:14:42.521894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.43 8
 
3.9%
3.78 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 83
40.5%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.4%
3.03 12
5.9%
3.05 6
2.9%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.4%
3.63 2
 
1.0%
3.62 23
11.2%
3.61 1
 
0.5%
3.6 1
 
0.5%

stroke
Real number (ℝ)

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2554146
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:42.940261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31359701
Coefficient of variation (CV)0.096330898
Kurtosis2.1743964
Mean3.2554146
Median Absolute Deviation (MAD)0.14
Skewness-0.68970458
Sum667.36
Variance0.098343087
MonotonicityNot monotonic
2024-05-12T09:14:43.299301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.4 20
 
9.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.11 6
 
2.9%
Other values (27) 87
42.4%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.4%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.8%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.4%
3.58 6
2.9%
3.54 4
2.0%
3.52 5
2.4%
3.5 6
2.9%
3.47 4
2.0%
3.46 8
3.9%

compressionratio
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:43.637543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2024-05-12T09:14:43.993584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

horsepower
Real number (ℝ)

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.11707
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:44.410467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.544167
Coefficient of variation (CV)0.37980483
Kurtosis2.6840062
Mean104.11707
Median Absolute Deviation (MAD)25
Skewness1.4053102
Sum21344
Variance1563.7411
MonotonicityNot monotonic
2024-05-12T09:14:44.860404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 117
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

peakrpm
Real number (ℝ)

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.122
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:45.215475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.98564
Coefficient of variation (CV)0.093068155
Kurtosis0.086755856
Mean5125.122
Median Absolute Deviation (MAD)300
Skewness0.075158722
Sum1050650
Variance227515.3
MonotonicityNot monotonic
2024-05-12T09:14:45.576452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.4%
Other values (13) 34
16.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

citympg
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:45.975963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2024-05-12T09:14:46.387783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.7%
27 14
 
6.8%
17 13
 
6.3%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highwaympg
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:46.892557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2024-05-12T09:14:47.280469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.8%
37 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

price
Real number (ℝ)

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.711
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-12T09:14:47.722101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.8523
Coefficient of variation (CV)0.60171925
Kurtosis3.0516479
Mean13276.711
Median Absolute Deviation (MAD)3306
Skewness1.7776782
Sum2721725.7
Variance63821762
MonotonicityNot monotonic
2024-05-12T09:14:48.180996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
9279 2
 
1.0%
7898 2
 
1.0%
8916.5 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (179) 185
90.2%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2024-05-12T09:14:16.137774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:09.285221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:14.486939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:18.777610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:23.402753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:28.134828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:32.404705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:36.637371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:40.675812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:45.251029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:49.851739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:53.905816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:58.219073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:02.624247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:06.620471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:11.241073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:16.449938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:09.915107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:14.793210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:19.084303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:23.689758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:28.420428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:32.679613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:36.904277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:40.965069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:45.574178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:50.112965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:54.192046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:58.507072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:02.881763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:06.939512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:11.596471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:16.728234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:10.281212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:15.077097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:19.365546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:23.974581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:28.677741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:32.913439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:37.145632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:41.202589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:45.820519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:50.373225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:54.436418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:58.943781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:03.121599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:07.210773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:11.985461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:17.059751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:10.612314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:15.321073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:19.647114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:24.261209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:28.956046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:33.167016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:37.414755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:41.499588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:46.126519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:50.629703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:54.704887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:59.222052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:03.364839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:07.505609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:12.314198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:17.355431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:10.904026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:15.589339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:19.923114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:24.558703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:29.265843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:33.599729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:37.675808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:41.816108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:46.650557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:50.883161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:54.971311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:59.500108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:03.626239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:07.798706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:12.771508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:17.634127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:11.187535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:15.867851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:20.190787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:24.813312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:29.522194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:33.844748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:37.914797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:42.092119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:46.895036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:51.121551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:55.232012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:59.757218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:03.868523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:08.068983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:13.050885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:17.929134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:11.495243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:16.140841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:20.599918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:25.093296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:29.769781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:34.083293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:38.154178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:42.374369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:47.150496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:51.484619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:55.501073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:00.020129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:04.116706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:08.346581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:13.309376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:18.253025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:11.768523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:16.395184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:20.848487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:25.356760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:30.011738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:34.328401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:38.387062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:42.630095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:47.398080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:51.713834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:55.758285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:00.283031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:04.346794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:08.620611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:13.590106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:18.550076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:12.032844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:16.658507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:21.095552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:25.848715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:30.247966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:34.565985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:38.624182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:42.906105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:47.644656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:51.952348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:56.016372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:00.589988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:04.575176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:08.882203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:13.859405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:18.859370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:12.322694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:16.935401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:21.353838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:26.140907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:30.517297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:34.835245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:38.879639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:43.190989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:47.919919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:52.202944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:56.302394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:00.884464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:14:14.166084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:19.128354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:12.579849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:17.165288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:21.782521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:26.391699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:30.738683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:35.059642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:39.104062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:43.431069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:48.168289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:13:56.554411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:01.120815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:05.058870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:14:14.424829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:19.566547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:12.880003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:17.447382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:22.057553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:26.699199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:31.009141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:35.326636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:39.370731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:43.722970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:48.438702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:52.665465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:56.844658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:01.378147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:05.320519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:09.726906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:14.715052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:19.931739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:13.178245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:17.698603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:22.297883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:26.959486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:31.244821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:35.564136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:14:05.557415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:09.986648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:14.988656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:20.203139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:13.468271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:17.926100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:22.522533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:13:36.050317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:13:44.598377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:49.211404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:14:15.549533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:13:18.476679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-12T09:13:27.828313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:32.046875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:36.325513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:40.371536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:44.908549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:49.559028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:53.634558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:13:57.921273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:02.350350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:06.307440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:10.906506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-12T09:14:15.834768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-05-12T09:14:21.290122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T09:14:22.573202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

car_IDsymbolingCarNamefueltypeaspirationdoornumbercarbodydrivewheelenginelocationwheelbasecarlengthcarwidthcarheightcurbweightenginetypecylindernumberenginesizefuelsystemboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgprice
013alfa-romero giuliagasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495.000
123alfa-romero stelviogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500.000
231alfa-romero Quadrifogliogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500.000
342audi 100 lsgasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950.000
452audi 100lsgasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450.000
562audi foxgasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250.000
671audi 100lsgasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710.000
781audi 5000gasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920.000
891audi 4000gasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875.000
9100audi 5000s (diesel)gasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.01605500162217859.167
car_IDsymbolingCarNamefueltypeaspirationdoornumbercarbodydrivewheelenginelocationwheelbasecarlengthcarwidthcarheightcurbweightenginetypecylindernumberenginesizefuelsystemboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgprice
195196-1volvo 144eagasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415.0
196197-2volvo 244dlgasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985.0
197198-1volvo 245gasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515.0
198199-2volvo 264glgasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420.0
199200-1volvo dieselgasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950.0
200201-1volvo 145e (sw)gasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845.0
201202-1volvo 144eagasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045.0
202203-1volvo 244dlgasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485.0
203204-1volvo 246dieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470.0
204205-1volvo 264glgasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625.0